Monazite-Type SmPO4_{4} as Potential Nuclear Waste Form: Insights into Radiation Effects from Ion-Beam Irradiation and Atomistic Simulations

Single-phase monazite-type ceramics are considered as potential host matrices for the conditioning of separated plutonium and minor actinides. Sm-orthophosphates were synthesised and their behaviour under irradiation was investigated with respect to their long-term performance in the repository environment. Sintered SmPO$_{4}$ pellets and thin lamellae were irradiated with 1, 3.5, and 7 MeV Au ions, up to fluences of 5.1 × 10$^{14}$ ions cm$^{-2}$ to simulate ballistic effects of recoiling nuclei resulting from α-decay of incorporated actinides. Threshold displacement energies for monazite-type SmPO$_{4}$ subsequently used in SRIM/TRIM simulations were derived from atomistic simulations. Raman spectra obtained from irradiated lamellae revealed vast amorphisation at the highest fluence used, although local annealing effects were observed. The broadened, but still discernible, band of the symmetrical stretching vibration in SmPO4 and the negligible increase in P–O bond lengths suggest that amorphisation of monazite is mainly due to a breaking of Ln–O bonds. PO$_{4}$ groups show structural disorder in the local environment but seem to behave as tight units. Annealing effects observed during the irradiation experiment and the distinctively lower dose rates incurred in actinide bearing waste forms and potential α-radiation-induced annealing effects indicate that SmPO$_{4}$-based waste forms have a high potential for withstanding amorphisation

The Colley-Tyndall Debate: A Synopsis of the Arguments of Both Speakers

https://digitalcommons.acu.edu/crs_books/1202/thumbnail.jp

Applications of Machine Learning to the Study of Crystalline Materials

This Special Issue, “Applications of Machine Learning to the Study of Crystalline Materials”, is a collection of seven original articles published in 2021 and 2022 and dedicated to applications of machine learning in materials research [...

Chemical Thermodynamics of Iron, Part 2

This is Volume 13b in the OECD Nuclear Energy Agency (NEA) “Chemical Thermodynamics” series. It is the second part of a critical review of the thermodynamic properties of iron, its solid compounds and aqueous complexes, initiated as part of the NEA Thermochemical Database Project Phase IV (TDB IV), and a continuation of Part 1, which was published in 2013 as volume 13a. The database system developed at the NEA Data Bank ensures consistency not only within the recommended data sets of iron, but also among all the data sets published in the series. This volume will be of particular interest to scientists carrying out performance assessments of deep geological disposal sites for radioactive waste

The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning

The aim of this work is to predict suitable chemical compositions for the development of new ceramic oxygen gas separation membranes, avoiding doping with toxic cobalt or expensive rare earths. For this purpose, we have chosen the system Sr1−xBax(Ti1−y−zVyFez)O3−δ (cubic perovskite-type phases). We have evaluated available experimental data, determined missing crystallographic information using bond-valence modeling and programmed a Python code to be able to generate training data sets for property predictions using machine learning. Indeed, suitable compositions of cubic perovskite-type phases can be predicted in this way, allowing for larger electronic conductivities of up to σe = 1.6 S/cm and oxygen conductivities of up to σi = 0.008 S/cm at T = 1173 K and an oxygen partial pressure pO2 = 10−15 bar, thus enabling practical applications

The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning

The aim of this work is to predict suitable chemical compositions for the development of new ceramic oxygen gas separation membranes, avoiding doping with toxic cobalt or expensive rare earths. For this purpose, we have chosen the system (Sr1−xBax)(Ti1−y−zVyFez)O3−δ (cubic perovskite-type phases). We have evaluated available experimental data, determined missing crystallographic information using bond-valence modeling and programmed a Python code to be able to generate training data sets for property predictions using machine learning. Indeed, suitable compositions of cubic perovskite-type phases can be predicted in this way, allowing for larger electronic conductivities of up to σe = 1.6 S/cm and oxygen conductivities of up to σi = 0.008 S/cm at T = 1173 K and an oxygen partial pressure pO2 = 10−15 bar, thus enabling practical applications

Structural Characterization of Geopolymers for the Safe Disposal of the FissionProducts 137Cs and 90Sr

Geopolymers are inorganic materials owning a high variability in structure and composition. Their resistance against heat and chemical attacks implies a high potential regarding their use as nuclear waste forms. First irradiation experiments by Lambertin et al. (J. Nuc. Mat. 2013, 443, 311-315) and Deng et al. (J. Nuc. Mat. 2015, 459, 270-275) confirmed a relatively high radiolysis resistance.Metakaolin based geopolymers are aluminosilicates with structural features of a three-dimensional network. The negative charges arising from partial substitution of tetravalent Si by trivalent Al are preferably balanced by alkaline and alkaline earth cations. Therefore a durable encapsulation and immobilization of the extremely mobile radionuclides 137Cs and 90Sr is anticipated.For structural characterization we synthesized geopolymers with varying chemical composition by mixing different metakaolin and kaolin powders with amorphous silica and alkaline solutions containing Na, K, Rb and Cs, or Ca and Sr, in different ratios, respectively. Additionally the water amount used for the syntheses was varied, as well as temperature and duration of thermal treatment during sample setting. Samples were characterized by XRD, Raman, IR and MAS-NMR spectroscopy, as well as SEM (EDX) and TG-DSC. MAS-NMR data show an evolution of the geopolymers’ structure that depends strongly on synthesis parameters and composition. Inversion recovery studies reveal two different Cs species with very similar signal positions, but different longitudinal relaxation times and therefore different magnetic vicinities. This might suggest mobile as well as immobile cation species

Monazite as a suitable actinide waste form

The conditioning of radioactive waste from nuclear power plants and in some countries even of weapons plutonium is an important issue for science and society. Therefore the research on appropriate matrices for the immobilization of fission products and actinides is of great interest. Beyond the widely used borosilicate glasses, ceramics are promising materials for the conditioning of actinides like U, Np, Pu, Am, and Cm. Monazite-type ceramics with general composition LnPO4 (Ln = La to Gd) and solid solutions of monazite with cheralite or huttonite represent important materials in this field. Monazite appears to be a promising candidate material, especially because of its outstanding properties regarding radiation resistance and chemical durability. This article summarizes the most recent results concerning the characterization of monazite and respective solid solutions and the study of their chemical, thermal, physical and structural properties. The aim is to demonstrate the suitability of monazite as a secure and reliable waste form for actinides

SciMesh: RDF graphs for scientific knowledge

SciMesh is a set of specifications that define the representation of scientific results in form of a knowledge graph. While many such data formats focus on simulation data, SciMesh explicitly includes physical specimens as first-class citizens. Thus, provenance of both data and specimens can be documented. SciMesh’s immediate purpose is to represent content of electronic lab notebooks (ELNs) for sharing scientific results across ELN instances. This way, collaboration partners running different ELNs (even different ELN software) can have a uniform view on their collective results without media disruptions